Green innovation has been deemed a key corporate capability to deal with environmental issues. The usage of advanced manufacturing technologies (AMT) provides important resources and knowledge for firms’ green innovation. Drawing on a resources-based approach, this study contributes to the existing literature by examining how the adoption of specific types of AMT (process, design, and planning) influences two dimensions of green innovation (green product innovation and green process innovation). In particular, we explore these relationships through internal environmental collaboration. Based on data collected from 198 Chinese manufacturing firms, we found that process, design, and planning AMT can contribute to both green products and process innovation. Moreover, the findings confirm the significant mediating role of internal environmental collaboration in this relationship. Specifically, internal environmental collaboration mediates the relationship between process AMT and green product innovation as well as the relationship between design AMT and two dimensions of green innovation; it also partially mediates the relationship between process AMT and green process innovation as well as the relationship between planning AMT and two dimensions of green innovation. These findings provide novel insights into how manufacturing firms can use various types of AMT to enhance their green innovation.

Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.

This paper focuses on the multi‐objective optimization of the reentrant hybrid flowshop scheduling problem (RHFSP) with machines turning on and off control strategy. RHFSP exhibits significance in many industrial applications, but scheduling with both energy consumption consideration and reentrant concept is relatively unexplored at present. In this study, an improved Multi‐Objective Multi‐Verse Optimizer (IMOMVO) algorithm is proposed to optimize the RHFSP with objectives of makespan, maximum tardiness, and idle energy consumption. To solve the proposed model more effectively, a series of improved operations are carried out, including population initialization based on Latin hypercube sampling (LHS), individual position updating based on Lévy flight, and chaotic local search based on logical self‐mapping. In addition, a right‐shift procedure is used to adjust the start time of operations aiming to minimize the idle energy consumption without changing the makespan. Then, Taguchi method is utilized to study the influence of different parameter settings on the scheduling results of the IMOMVO algorithm. Finally, the performance of the proposed IMOMVO algorithm is evaluated by comparing it with MOMVO, MOPSO, MOALO, and NSGA‐II on the same benchmark set. The results show that IMOMVO algorithm can solve the RHFSP with machines turning on and off control strategy effectively, and in terms of convergence and diversity of non‐dominated solutions, IMOMVO is obviously superior to other algorithms. However, the distribution level of the five algorithms has little difference. Meanwhile, by turning on and off the machine properly, the useless energy consumption in the production process can be reduced effectively.

A business delivery model with professional vehicles as well as occasional passing-by vehicles is investigated in this paper. The drivers deliver parcels from the distribution center to customers and the passing-by driver can get a certain amount of compensation in return. To give a satisfactory solution from the perspective of platform owner, customers, professional drivers, occasional drivers, and authority, a multi-layer comprehensive model is proposed. To effectively solve the proposed model, we introduce an improved variable neighborhood search (VNS) with a memory-based restart mechanism. The new algorithm is evaluated on instances derived from Solomon’s benchmark and real-life beer delivery instances. Taguchi experiment is used to tune parameters in the proposed VNS, followed by component analysis and real-life experiments. Experimental results indicate that the proposed strategies are effective and the new delivery model in this paper has some advantages over traditional and single-delivery ones from the comprehensive perspectives of stakeholders in the crowdsourcing logistics system.

In the face of demand disruptions, dual-channel supply chains (SCs) that lack resilience may be more vulnerable. Reaching moderate SC resilience through coordination is essential for dealing with disruptions. This paper investigates the operation management of a dual-channel fresh-food SC (FSC) under disruption. The centralized and decentralized decision models propose joint quality efforts based on the consideration of quality preference and loss. From the perspective of SC resilience, we analyze how SC members can optimally make price, quality, and quantity decisions resiliently and robustly under the disruption of quality preference. The results show that (1) no matter the kind of decision model, considering quality preference disruptions can significantly increase the SC profit; (2) there is a resilience range in decisions with the influence of the disruption cost. The original optimal decisions in the resilience range are robust and sustain SC performance without change; and (3) the disruption significantly impacts offline channel retailers, who are at a disadvantage when competing with online channels. A centralized decision model can achieve higher profits and quality levels in response to demand disruptions. This paper extends the concept of resilience to the FSC and provides suggestions for fresh-food enterprises to conduct quality efforts and cope with demand interruption.

Climate change is a serious challenge facing the world today. Countries are already working together to control carbon emissions and mitigate global warming. Improving energy efficiency is currently one of the main carbon reduction measures proposed by the international community. Within this context, improving energy efficiency in manufacturing systems is crucial to achieving green and low-carbon transformation. The aim of this work is to develop a new preventive maintenance strategy model. The novelty of the model is that it takes into account energy efficiency, maintenance cost, product quality, and the impact of recycling defective products on energy efficiency. Based on the relationship between preventive maintenance cost, operating energy consumption, and failure rate, the correlation coefficient is introduced to obtain the variable preventive maintenance cost and variable operating energy consumption. Then, the cost and energy efficiency models are established, respectively, and finally, the Pareto optimal solution is found by the nondominated sorting genetic algorithm II (NSGAII). The results show that the preventive maintenance strategy proposed in this paper is better than the general maintenance strategy and more relevant to the actual situation of manufacturing systems. The scope of the research in this paper can support the decision of making energy savings and emission reductions in the manufacturing industry, which makes the production, maintenance, quality, and architecture of the manufacturing industry optimized.